环境科学
含水量
蒸散量
气候学
降水
干旱
数据同化
水分
气候变化
植被(病理学)
卫星
大气科学
气象学
地理
地质学
工程类
海洋学
病理
航空航天工程
古生物学
生物
医学
岩土工程
生态学
作者
Yansong Guan,Xihui Gu,Louise Slater,Jianfeng Li,Dongdong Kong,Xiang Zhang
标识
DOI:10.1016/j.jhydrol.2023.130095
摘要
Accurate soil moisture datasets are essential to understand the impacts of climate change. However, few studies have evaluated the consistency and drivers of long-term trends in soil moisture among different dataset types (satellite, assimilation, reanalysis, and climate model) at the global scale. Here we analyze the spatio-temporal variations of global surface soil moisture and associated climate dynamics over 1980–2020 using multiple soil moisture datasets, i.e., multi-satellite assimilated remote sensing datasets (ESA CCI), simulated soil moisture based on LSMs (GLDAS, GLEAM, CMIP6), and reanalysis (ECMWF ERA5, MERRA2, CRA-Land). Most of these datasets indicate pervasive drying of global surface soil moisture over the last four decades. Prominent soil moisture drying is detected in North America, Europe, northeastern Asia, North Africa, and the Arabian Peninsula. The cross-correlations among the five synthetic soil moisture datasets are the highest between GLEAM and the reanalysis datasets. Using the Aridity Index (AI, the ratio between annual total precipitation and potential evapotranspiration), we find that soil moisture drying is the most intensive in the humid-arid transitional regions with AI ranging 0.8–1.2. Surface soil moisture drying is primarily driven by increases in temperature, followed by ENSO, as indicated by Maximum Covariance Analysis (MCA). However, the significance of the impact of ENSO on soil moisture variability is sensitive to the choice of soil moisture dataset used in the MCA.
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